Task Scheduling Approach Research Articles

HGPSO: An efficient scientific workflow scheduling in cloud environment using a hybrid optimization algorithm

Cloud technology has raised significant prominence providing a unique market economic approach for resolving large-scale challenges in heterogeneous distributed systems. Through the use of the network, it delivers secure, quick, and profitable information storage with computational capability. Cloud applications are available on-demand to meet a variety of user QoS standards. Due to a large number of users and tasks, it is important to achieve efficient scheduling of tasks submitted by users. One of the most important and difficult non-deterministic polynomial-hard challenges in cloud technology is task scheduling. Therefore, in this paper, an efficient task scheduling approach is developed. To achieve this objective, a hybrid genetic algorithm with particle swarm optimization (HGPSO) algorithm is presented. The scheduling is performed based on the multi-objective function; the function is designed based on three parameters such as makespan, cost, and resource utilization. The proper scheduling system should minimize the makespan and cost while maximizing resource utilization. The proposed algorithm is implemented using WorkflowSim and tested with arbitrary task graphs in a simulated setting. The results obtained reveal that the proposed HGPSO algorithm outperformed all available scheduling algorithms that are compared across a range of experimental setups.

Two-Stage Optimal Task Scheduling for Smart Home Environment Using Fog Computing Infrastructures

The connection of many devices has brought new challenges with respect to the centralized architecture of cloud computing. The fog environment is suitable for many services and applications for which cloud computing does not support these well, such as: traffic light monitoring systems, healthcare monitoring systems, connected vehicles, smart cities, homes, and many others. Sending high-velocity data to the cloud leads to the congestion of the cloud infrastructure, which further leads to high latency and violations of the Quality-of-Service (QoS). Thus, delay-sensitive applications need to be processed at the edge of the network or near the end devices, rather than the cloud, in order to provide the guaranteed QoS related to the reduced latency, increased throughput, and high bandwidth. The aim of this paper was to propose a two-stage optimal task scheduling (2-ST) approach for the distribution of tasks executed within smart homes among several fog nodes. To effectively solve the task scheduling, this proposed approach uses a naïve-Bayes-based machine learning model for training in the first stage and optimization in the second stage using a hyperheuristic approach, which is a combination of both Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). In addition, the proposed mechanism was validated against various metrics such as energy consumption, latency time, and network usage.

An enhanced ordinal optimization with lower scheduling overhead based novel approach for task scheduling in cloud computing environment

Efficient utilization of available computing resources in Cloud computing is one of the most challenging problems for cloud providers. This requires the design of an efficient and optimal task-scheduling strategy that can play a vital role in the functioning and overall performance of the cloud computing system. Optimal Schedules are specifically needed for scheduling virtual machines in fluctuating & unpredictable dynamic cloud scenario. Although there exist numerous approaches for enhancing task scheduling in the cloud environment, it is still an open issue. The paper focuses on an improved & enhanced ordinal optimization technique to reduce the large search space for optimal scheduling in the minimum time to achieve the goal of minimum makespan. To meet the current requirement of optimal schedule for minimum makespan, ordinal optimization that uses horse race conditions for selection rules is applied in an enhanced reiterative manner to achieve low overhead by smartly allocating the load to the most promising schedule. This proposed ordinal optimization technique and linear regression generate optimal schedules that help achieve minimum makespan. Furthermore, the proposed mathematical equation, derived using linear regression, predicts any future dynamic workload for a minimum makespan period target.

An IFWA-BSA Based Approach for Task Scheduling in Cloud Computing

Establishing an efficient cloud computing task scheduling model is the object of many scholars’ research. In view of the low scheduling efficiency in cloud computing task scheduling, we propose a cloud computing task scheduling algorithm based on the fusion of the Fireworks Algorithm and Bird Swarm Algorithm (IFWA-BSA). Firstly, we describe the cloud computing task scheduling model based on time and cost constraint functions, secondly, we use chaotic backward learning and Coasean distribution for optimization in FWA initialization; we set thresholds for the radius of core fireworks and non-core fireworks for optimization; we filter the IFWA individuals after each iteration by BSA algorithm, and finally, we use the IFWA-BSA algorithm is used in cloud computing task scheduling model to solve the optimal solution. In the simulation experiments, IFWA-BSA has obvious advantages over ACO, PSO and FWA in the comparison of execution time and consumption cost indexes, which reduces the scheduling time and cost of cloud computing.

Task Scheduling Approach in Cloud Computing Environment Using Hybrid Differential Evolution

Task scheduling is one of the most significant challenges in the cloud computing environment and has attracted the attention of various researchers over the last decades, in order to achieve cost-effective execution and improve resource utilization. The challenge of task scheduling is categorized as a nondeterministic polynomial time (NP)-hard problem, which cannot be tackled with the classical methods, due to their inability to find a near-optimal solution within a reasonable time. Therefore, metaheuristic algorithms have recently been employed to overcome this problem, but these algorithms still suffer from falling into a local minima and from a low convergence speed. Therefore, in this study, a new task scheduler, known as hybrid differential evolution (HDE), is presented as a solution to the challenge of task scheduling in the cloud computing environment. This scheduler is based on two proposed enhancements to the traditional differential evolution. The first improvement is based on improving the scaling factor, to include numerical values generated dynamically and based on the current iteration, in order to improve both the exploration and exploitation operators; the second improvement is intended to improve the exploitation operator of the classical DE, in order to achieve better results in fewer iterations. Multiple tests utilizing randomly generated datasets and the CloudSim simulator were conducted, to demonstrate the efficacy of HDE. In addition, HDE was compared to a variety of heuristic and metaheuristic algorithms, including the slime mold algorithm (SMA), equilibrium optimizer (EO), sine cosine algorithm (SCA), whale optimization algorithm (WOA), grey wolf optimizer (GWO), classical DE, first come first served (FCFS), round robin (RR) algorithm, and shortest job first (SJF) scheduler. During trials, makespan and total execution time values were acquired for various task sizes, ranging from 100 to 3000. Compared to the other metaheuristic and heuristic algorithms considered, the results of the studies indicated that HDE generated superior outcomes. Consequently, HDE was found to be the most efficient metaheuristic scheduling algorithm among the numerous methods researched.

A multi‐queue priority‐based task scheduling algorithm in fog computing environment

AbstractFog computing is a novel, decentralized and heterogeneous computing environment that extends the traditional cloud computing systems by facilitating task processing near end‐users on computing resources called fog nodes. These diverse and resource‐constrained fog devices process a large volume of tasks generated by various fog applications. These tasks are generated by various applications, some of which may be latency‐sensitive, while others may tolerate some degree of delay in their normal functions. Task scheduling determines when a task should be allocated to a computing resource and how long that task can occupy the assigned resource. The majority of task scheduling algorithms focus on prioritizing the latency‐sensitive tasks only, which results in the long waiting time for the other type of tasks. Hence, these priority‐based schedulers cause task starvation for less important tasks while achieving delay‐optimal results for latency‐sensitive tasks. As a result, in this paper, we propose MQP, a multi‐queue priority‐based preemptive task scheduling approach that achieves a balanced task allocation for those applications that can tolerate a certain amount of processing delay and the latency‐sensitive fog applications. At run‐time, the MQP algorithm categorizes tasks as short and long based on their burst time. MQP algorithm maintains a separate task queue for each task category and dynamically updates the time slot value for preemption. The proposed technique's major purpose is to reduce response time for those data‐intensive applications in the fog computing environment, which include both latency‐sensitive tasks and tasks which are less latency‐sensitive, thereby addressing the starvation problem for less latency‐sensitive tasks. A smart traffic management case study is created to model a scenario with both latency‐sensitive short and less latency‐sensitive long tasks. We implement the MQP algorithm using iFogSim and confirm that it reduces the service latencies for long tasks. Simulation results show that the MQP algorithm allocates tasks to a fog device more efficiently and reduces the service latencies for long tasks. The average value of percentage reduction in the latency across all experimental configurations achieved is 22.68% and 38.45% in comparison to First Come‐First Serve and shortest job first algorithms.

Designing and Implementation of Failure-Aware Based Approach for Task Scheduling in Grid Computing

Grid computing makes large-scale computations easier to handle. In heterogeneous systems like grid computing, failure is inevitable. Because of the volume and diversity of the resources, scheduling algorithm is among the most difficult challenges to overcome in grid computing. To reduce the make-span of the job to be executed a thorough understanding of scheduling in grid is important. Say there are two computing nodes that aren't being used right now. The scheduler may choose the node that has higher computing strength (for example, higher CPU speed, higher free memory), even though this node may also have high potential of failure. High potential of failure refers to the possibility of the failure occurring at execution time, resulting in the decrease of system performance. Therefore, awareness of failure is also very important in scheduling. This work proposes and implements a failure-aware scheduling method to schedule the tasks which uses both performance factors and failure factors of resources while making scheduling decision. The proposed algorithm is analyzed over various performance matrices and it shows considerably improved performance over existing algorithm

Scheduling of Missions with Constrained Tasks for Heterogeneous Robot Systems

We present a formal tasK AllocatioN and scheduling apprOAch for multi-robot missions (KANOA). KANOA supports two important types of task constraints: task ordering, which requires the execution of several tasks in a specified order; and joint tasks, which indicates tasks that must be performed by more than one robot. To mitigate the complexity of robotic mission planning, KANOA handles the allocation of the mission tasks to robots, and the scheduling of the allocated tasks separately. To that end, the task allocation problem is formalised in first-order logic and resolved using the Alloy model analyzer, and the task scheduling problem is encoded as a Markov decision process and resolved using the PRISM probabilistic model checker. We illustrate the application of KANOA through a case study in which a heterogeneous robotic team is assigned a hospital maintenance mission.

A new approach for global task scheduling in volunteer computing systems

A new approach for global task scheduling in volunteer computing systems

DSTS: A hybrid optimal and deep learning for dynamic scalable task scheduling on container cloud environment

Containers have grown into the most dependable and lightweight virtualization platform for delivering cloud services, offering flexible sorting, portability, and scalability. In cloud container services, planner components play a critical role. This enhances cloud resource workloads and diversity performance while lowering costs. We present hybrid optimum and deep learning approach for dynamic scalable task scheduling (DSTS) in container cloud environment in this research. To expand containers virtual resources, we first offer a modified multi-swarm coyote optimization (MMCO) method, which improves customer service level agreements. Then, to assure priority-based scheduling, we create a modified pigeon-inspired optimization (MPIO) method for task clustering and a rapid adaptive feedback recurrent neural network (FARNN) for pre-virtual CPU allocation. Meanwhile, the task load monitoring system is built on a deep convolutional neural network (DCNN), which allows for dynamic priority-based scheduling. Finally, the presentation of the planned DSTS methodology will be estimated utilizing various test vectors, and the results will be associated to present state-of-the-art techniques.

An optimistic approach for task scheduling in cloud computing

An optimistic approach for task scheduling in cloud computing

An energy-efficient scheduling approach for memory-intensive tasks in multi-core systems

An energy-efficient scheduling approach for memory-intensive tasks in multi-core systems

A Hybrid Approach for Task Scheduling in the Cloud Environment

In today’s world, Cloud Computing has been considered as the best concept for the virtualization of various resources. There are various approaches that have been available for improvising the load balancing and also to improvise the job scheduling in the concept of cloud. Cloud Computing has offers the on-demand allocation of resources to users and this feature of cloud makes it a best among various technologies. Cloud computing provides great performance in less maintenance cost. So, Task scheduling has become a very important factor in enhancing the performance of resources dynamically and at low cost, which is the most crucial part of cloud computing. In other words, we can say that allocation of resources and task scheduling are the two major factors that are considered for the better performance and they must be organized precisely. The author has focused on enhancing the task scheduling process by creating a hybrid optimization algorithm and named as Cuckoo Harmony Search Algorithm (CHSA) to remove the task scheduling problem.

RASA: Reliability-Aware Scheduling Approach for FPGA-Based Resilient Embedded Systems in Extreme Environments

Field-programmable gate arrays (FPGAs) offer the flexibility of general-purpose processors along with the performance efficiency of dedicated hardware that essentially renders it as a platform of choice for modern-day robotic systems for achieving real-time performance. Such robotic systems when deployed in harsh environments often get plagued by faults due to extreme conditions. Consequently, the real-time applications running on FPGA become susceptible to errors which call for a reliability-aware task scheduling approach, the focus of this article. We attempt to address this challenge using a hybrid offline-online approach. Given a set of periodic real-time tasks that require to be executed, the offline component generates a feasible preemptive schedule with specific preemption points. At runtime, these preemption events are utilized for fault detection. Upon detecting any faulty execution at such distinct points, the reliability-aware scheduling approach, RASA, orchestrates the recovery mechanism to remediate the scenario without jeopardizing the predefined schedule. Effectiveness of the proposed strategy has been verified through simulation-based experiments and we observed that the RASA is able to achieve 72% of task acceptance rate even under 70% of system workloads with high fault occurrence rates.

An Opposition-Based Hybrid Evolutionary Approach for Task Scheduling in Fog Computing Network

An Opposition-Based Hybrid Evolutionary Approach for Task Scheduling in Fog Computing Network

A systematic review of task scheduling approaches in fog computing

AbstractDue to increased use of IoT devices and data sensors a huge amount of data is being produced for processing in real‐time. Fog computing has evolved as a solution for fast processing of data. To complete the data processing as per requirement of user, it is processed at fog nodes which are near the user. To complete work in a specified time with limited resources, task scheduling is performed. With the increased amount of data to be processed, the completion of task within given time in fog computing is a major challenge. So, scheduling of tasks and resources is a very important issue. A lot of research has been undertaken in recent years. In this survey, authors have reviewed various task scheduling algorithms suggested by researchers to meet the user requirements. The focus of this article is on diverse scheduling techniques being deployed in fog computing. An effort has been made to classify existing approaches, research issues and determine significant issues existing in this field. There are four major scheduling categories that are used by researchers namely, static, dynamic, heuristic, and hybrid. As per this study, 17% researchers deployed static, 23% dynamic, 47% heuristic, and 13% hybrid approaches, respectively. Analysis shows that for QoS (Quality of Service) parameters 19% researchers are focused on response time, 18% on cost and energy consumption, and 16% on makespan. For QoS parameter other factors have much smaller contribution in comparison to the above factors. In the tools used, researchers observed that 40% of the researches have used iFogSim, according to literature. Besides, this research article also discusses open issues and future work in the field of fog computing. This article also underlines various open issues and future directions in the field of fog computing.

Multiagent RL Aided Task Offloading and Resource Management in Wi-Fi 6 and 5G Coexisting Industrial Wireless Environment

With the emergence of industrial Internet of Things (IIoT), intensive computation workload will be imposed to industrial end units (IEUs). By leveraging mobile edge computing (MEC), the local computational tasks can be offloaded to servers deployed in mobile edge networks with low latency. This article proposes the intelligent cost-and-energy-effective task offloading in the 5G and Wi-Fi 6 coexisting heterogeneous IIoT networks. The novel joint task scheduling and resource allocation approach comprises the following two parts: a Lyapunov optimization-based component to decide local task scheduling and computing power and an online multiagent reinforcement learning component together with a game theory-based algorithm to select offloading link and decide transmit power, respectively. Simulation results demonstrate the proposed approach holds obvious advantage over the compared “intuition” and “cost optimal” approaches in the efficiency of making comprehensive decision that improves energy efficiency and cost while controlling task delay in the multi-IEU and multiaccess-node MEC systems.

Energy-aware task scheduling and offloading using deep reinforcement learning in SDN-enabled IoT network

The fifth-generation (5G) mobile network services have made tremendous growth in the Internet of Things (IoT) network. A counters number of battery-powered IoT devices are deployed to serve diverse scenarios, e.g., smart cities, autonomous farming, smart manufacturing, to name but a few. In this context, energy consumption became one of the most critical concerns in interconnecting smart IoT devices in such scenarios. Additionally, whenever these IoT devices are distributed in space and time-evolving, they are expected to deliver high volume data scalably/predictably while minimizing end-to-end latency. Furthermore, edge IoT nodes often face the biggest hurdle of performing optimal resource distribution and achieving high-performance levels while coping with the variability of task handling, energy conservation, and ultra-reliable low-latency.This paper investigates an energy-aware and low-latency oriented computing task scheduling problem in a Software-Defined Fog-IoT Network. First, we formulate the online task assignment and scheduling problem as an energy-constrained Deep Q-Learning process as a kickoff. The latter strives to minimize the network latency while ensuring energy efficiency by saving battery power under the constraints of application dependence. Then, given the task arrival process, we introduce a deep reinforcement learning (DRL) approach for dynamic task scheduling and assignment in the Software-Defined Networking (SDN)-enabled edge networks. We conducted comprehensive experiments and compared the introduced algorithm to three pioneering deep learning algorithms (i.e., deterministic, random, and A3C agents). Extensive simulation results demonstrated that our proposed solution outperforms these algorithms. Additionally, we highlight the characterizing feature of our design, energy-awareness, as it offers better energy-saving by up to 87% compared against the other approaches. We have shown that the offloading scheme could perform more task assignments with the available battery power by up to 50% less time delay. Our results back our claims that the solution we propose can readily be used to dynamically optimize task scheduling and assignment of complex jobs with task dependencies in distributed Fog IoT networks.

A distributed quality of service-enabled load balancing approach for cloud environment

PurposeDuring high demand for the virtualized resources in cloud environment, efficient task scheduling achieves the desired performance criteria by balancing the load in the system.Design/methodology/approachIt is a task scheduling approach used for load balancing in cloud environment. Task scheduling in such an environment is used for the task execution on a suitable resource by considering some parameters and constraints to achieve performance.FindingsThe presented mechanism is an extension of the previous proposed work quality of service (QoS)-enabled join minimum loaded queue (JMLQ) (Sharma et al., 2019c). The proposed approach has been tested in the CloudSim simulator, and the results show that the proposed approach achieves better results in comparison to QoS-enabled JMLQ and its other variants in the cloud environment.Originality/value90%

MOTS‐ACO: An improved ant colony optimiser for multi‐objective task scheduling optimisation problem in cloud data centres

Task scheduling in cloud data centres is an optimisation problem that aims to minimise power consumption and task makespan as well as ensures the quality of service delivered to cloud consumers. Although there are several existing task scheduling approaches, these methods mainly focus on optimising makespans of tasks while ignoring critical issues. This paper presents a comprehensive multi-objective task scheduling model based on an improved Ant Colony Optimisation (ACO) algorithm, referred to as MOTS-ACO. In order to promote the diversity of the Pareto set and accelerate the convergence speed, adaptive distribution probability is incorporated into the proposed algorithm, specifically in the process of updating the global rule. The performance of MOTS-ACO is compared with several existing multi-objectives task scheduling algorithms based on the makespan time, turnaround time, power efficiency and load balancing parameters. The results show the superiority of MOTS-ACO in terms of the makespan time, turnaround time, power efficiency and load balancing. Moreover, the proposed MOTS-ACO algorithm introduces more diversity in the search and accelerates the convergence speed towards the Pareto optimal solution.